Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	18,314
2	New Jersey	16,666
3	Massachusetts	12,629
4	Rhode Island	12,078
5	Connecticut	10,690
6	District of Columbia	10,301
7	Delaware	8,081
8	Illinois	7,637
9	Louisiana	7,466
10	Maryland	6,596
11	Nebraska	5,492
12	Pennsylvania	5,208
13	Michigan	5,192
14	Iowa	4,739
15	South Dakota	4,552
16	Indiana	4,292
17	Colorado	3,850
18	Mississippi	3,841
19	Virginia	3,648
20	Georgia	3,450
21	New Mexico	2,907
22	Minnesota	2,904
23	Kansas	2,896
24	New Hampshire	2,685
25	Washington	2,605
26	Tennessee	2,601
27	North Dakota	2,533
28	Alabama	2,464
29	Ohio	2,434
30	Utah	2,303
31	Nevada	2,292
32	Wisconsin	2,184
33	Florida	2,161
34	California	2,073
35	Arizona	1,946
36	Missouri	1,828
37	North Carolina	1,823
38	Kentucky	1,799
39	South Carolina	1,736
40	Texas	1,717
41	Arkansas	1,594
42	Vermont	1,506
43	Idaho	1,373
44	Oklahoma	1,364
45	Wyoming	1,323
46	Maine	1,274
47	Oregon	874
48	Puerto Rico	848
49	West Virginia	838
50	Alaska	545
51	Hawaii	444
52	Montana	439

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Connecticut	189
2	District of Columbia	188
3	Rhode Island	181
4	Massachusetts	175
5	Delaware	169
6	Illinois	164
7	New Jersey	155
8	Maryland	153
9	Nebraska	148
10	Minnesota	126
11	Virginia	96
12	Iowa	95
13	New York	91
14	Indiana	80
15	North Dakota	74
16	Mississippi	70
17	New Mexico	68
18	Pennsylvania	65
19	Louisiana	62
20	Michigan	62
21	North Carolina	61
22	Colorado	55
23	South Dakota	52
24	Kansas	49
25	Wisconsin	49
26	Alabama	48
27	Utah	47
28	New Hampshire	46
29	Arizona	45
30	Georgia	43
31	Ohio	42
32	California	41
33	Tennessee	41
34	Arkansas	38
35	Florida	35
36	Missouri	35
37	Kentucky	34
38	Nevada	34
39	South Carolina	34
40	Texas	32
41	Wyoming	28
42	Maine	27
43	Oklahoma	26
44	Washington	26
45	Puerto Rico	17
46	Idaho	12
47	Oregon	11
48	West Virginia	10
49	Alaska	5
50	Vermont	3
51	Montana	1
52	Hawaii	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,454
2	New Jersey	1,174
3	Connecticut	967
4	Massachusetts	850
5	District of Columbia	555
6	Louisiana	524
7	Michigan	492
8	Rhode Island	477
9	Pennsylvania	356
10	Illinois	335
11	Maryland	334
12	Delaware	305
13	Indiana	262
14	Colorado	212
15	Mississippi	177
16	Georgia	152
17	Ohio	141
18	Washington	133
19	Minnesota	131
20	New Mexico	128
21	New Hampshire	126
22	Nevada	118
23	Virginia	118
24	Iowa	112
25	Alabama	99
26	Missouri	99
27	Arizona	94
28	Florida	92
29	Vermont	86
30	California	84
31	Kentucky	79
32	Wisconsin	78
33	South Carolina	75
34	Oklahoma	72
35	Nebraska	68
36	Kansas	65
37	North Carolina	65
38	North Dakota	57
39	Maine	52
40	South Dakota	49
41	Texas	46
42	Tennessee	43
43	Idaho	41
44	Puerto Rico	38
45	West Virginia	37
46	Arkansas	33
47	Oregon	32
48	Utah	24
49	Wyoming	17
50	Montana	14
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Connecticut	15
2	Massachusetts	13
3	District of Columbia	11
4	New Jersey	11
5	New York	9
6	Delaware	8
7	Rhode Island	8
8	Maryland	6
9	Colorado	4
10	Illinois	4
11	Louisiana	4
12	Indiana	3
13	Michigan	3
14	Mississippi	3
15	New Hampshire	3
16	Pennsylvania	3
17	Iowa	2
18	Minnesota	2
19	Nevada	2
20	New Mexico	2
21	Ohio	2
22	Arizona	1
23	California	1
24	Florida	1
25	Georgia	1
26	Missouri	1
27	Nebraska	1
28	Virginia	1
29	Wyoming	1
30	Alabama	0
31	Alaska	0
32	Arkansas	0
33	Hawaii	0
34	Idaho	0
35	Kansas	0
36	Kentucky	0
37	Maine	0
38	Montana	0
39	North Carolina	0
40	North Dakota	0
41	Oklahoma	0
42	Oregon	0
43	Puerto Rico	0
44	South Carolina	0
45	South Dakota	0
46	Tennessee	0
47	Texas	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	West Virginia	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	122,563	1	99
Dakota	Nebraska	78,148	2	99
Lincoln	Arkansas	74,094	3	99
Nobles	Minnesota	64,127	4	99
Lake	Tennessee	57,583	5	99
Davidson	Tennessee	5,993	259	91
Richland	South Carolina	3,007	609	80
Pierce	Washington	1,998	885	71
Orange	California	1,406	1171	62
York	South Carolina	1,082	1417	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Randolph	Georgia	3,098	1	99
Terrell	Georgia	2,813	2	99
Early	Georgia	2,748	3	99
Essex	New Jersey	1,935	4	99
Nassau	New York	1,865	5	99
Richland	South Carolina	147	512	83
Pierce	Washington	75	807	74
Davidson	Tennessee	63	913	70
Orange	California	28	1283	59
York	South Carolina	18	1453	53

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.